计算机与数字工程2025，Vol.53Issue(4)：1076-1080,5.DOI:10.3969/j.issn.1672-9722.2025.04.027
基于深度学习的不良坐姿识别研究
Research on Bad Sitting Pose Recognition Based on Deep Learning
摘要
Abstract
In view of the current situation where the incidence of physical diseases caused by poor sitting postures is constant-ly rising,this paper constructs a system for identifying poor sitting postures by integrating ShuffleNetV2,the attention mechanism and the Long Short-Term Memory Network(LSTM).Integrating the attention mechanism into the network structure of ShuffleNetV2 enables the adjusted model to be used for the estimation of human postures.Then,the obtained human skeleton sequences are pro-cessed by the Long Short-Term Memory Network to obtain the final classification results of sitting postures.The experiment results show that the performance of algorithm is good and efficient on self-structure database of single person poses.By using this data-base,the light-weight recognition system for bad sitting pose is realized.关键词
ShuffleNetV2/人体姿态估计/注意力机制/骨架动作识别/不良坐姿检测Key words
ShuffleNetV2/human pose estimation/attention mechanism/skeleton based action recognition/bad sitting pose recognition分类
信息技术与安全科学引用本文复制引用
陈天宇,于向军..基于深度学习的不良坐姿识别研究[J].计算机与数字工程,2025,53(4):1076-1080,5.
